Special Issue "Computational Modeling Approaches to Finance and Fintech Innovation"

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: 22 March 2022.

Special Issue Editors

Prof. Evangelos Katsamakas
E-Mail Website
Guest Editor
Gabelli School of Business, Fordham University, New York, NY 10023, USA
Interests: digital transformation; platforms and ecosystems; fintech; economics of technology; dynamics of complex systems; computational modeling; data science
Special Issues and Collections in MDPI journals
Prof. Dr. Oleg Pavlov
E-Mail Website
Guest Editor
Worcester Polytechnic Institute, Worcester, MA 01609, USA
Interests: system dynamics; systems thinking; economics; higher education; service science

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed the tremendous growth of financial and fintech innovations that are transforming financial services, financial markets and the global economy.

We invite high-quality research submissions that study all aspects of finance and fintech innovation. Methodologically, we are especially interested in computational modeling and simulation approaches, including system dynamics, agent-based modeling, network models, machine learning, natural language processing, etc. We encourage interdisciplinary research that appreciates complex systems and seeks to understand, explain, design and/or forecast system behavior. The research should have clear practical implications and it should help managers, regulators and policy-makers make better decisions and create more value, while navigating the complex fintech landscape and its implications.

A list of suggested topics includes the following:

  • Trading and algorithmic trading
  • AI/machine learning in banking
  • Blockchains and applications
  • Smart contracts
  • Investment advice and robo-advisers
  • Fintech applications
  • Payment systems
  • Bitcoin, cryptocurrencies, CBDC, digital assets, NFTs
  • Decentralized finance
  • Designing fintech products and customer experience
  • Fintech and financial markets
  • Fintech startups
  • Digitalization and Digital transformation of financial services firms and markets
  • Social media, Cloud, Mobile, IoT, AR/VR and fintech
  • Big data, predictive analytics, data visualization in financial services
  • Financial and risk analytics
  • Open banking and APIs
  • Platforms and ecosystems
  • Crowdfunding
  • P2P lending
  • Fintech and cybersecurity
  • BigTech and finance
  • Dynamics of financial instability
  • Fintech economic and social impact
  • Fintech for good (social finance, green finance, social innovation, financial inclusion, responsible investing etc.)
  • Regulation of fintech and Regtech
  • Covid-19 and fintech

We also encourage submissions on other topics related to the theme of the Special Issue.

Prof. Evangelos Katsamakas
Prof. Oleg Pavlov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Article
Startup Investment Decision Support: Application of Venture Capital Scorecards Using Machine Learning Approaches
Systems 2021, 9(3), 55; https://doi.org/10.3390/systems9030055 - 22 Jul 2021
Viewed by 496
Abstract
This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard [...] Read more.
This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm. Full article
(This article belongs to the Special Issue Computational Modeling Approaches to Finance and Fintech Innovation)
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